约束不确定非线性系统的鲁棒优化镇定

针对状态和输入约束不确定非线性仿射系统,提出一种鲁棒镇定的优化控制器设计方法.基于弱鲁棒控制Lyapunov函数概念,构造一个参数可调控制器.再利用LaSalle定理和逆优化理论,验证该控制器的鲁棒镇定性和逆最优性.进一步,采用滚动优化原理在线计算控制器的可调参数,实现闭环系统的鲁棒优化镇定.最后对一个开环不稳定振荡器系统进行鲁棒优化镇定,其结果验证了文中方法的有效性.     

积分二次约束下系统鲁棒镇定的凸参数化方法

研究了积分二次约束下不确定系统的鲁棒控制器设计问题. 通过将控制器的Youla参数化方法与鲁棒稳定性频域判据相结合, 将鲁棒控制器设计问题转化为RH∞空间的凸可行性问题, 进而将该问题转化为求解频域线性矩阵不等式的可行解问题. 在此基础上, 利用有理函数矩阵边界插值方法求得鲁棒控制器.     

含参数不确定性系统的鲁棒性能设计

讨论含有参数不确定性系统的鲁棒性能设计问题。首先将该问题转化为一个多项式簇的鲁棒稳定性问题，从而得到鲁棒性能控制器应满足的频域充要条件，然后通过一个双回路控制器的设计将鲁棒镇定和鲁棒性能设计分两步进行，使设计条理化、简单化。     

基于LMI的鲁棒控制器设计

研究了时域线性系统的鲁棒控制分析与综合,提出了将鲁棒控制设计问题转化为线性矩阵不等式（ＬＭＩ）形式的方法,给出了鲁棒控制器的标准ＬＭＩ结构,并通过求解ＬＭＩ得到鲁棒控制棒。文中以双转子涡喷发动机控制器设计为例风言风语地基于ＬＭＩ的鲁棒控制器,表明所给方法是可行的。     

离散区间系统的H ∞ 鲁棒控制

研究离散区间系统的鲁棒稳定性分析和鲁棒控制问题,首先基于Ｒｉｃｃａｔｉ方程方法讨论系统的鲁棒稳定性,得到了检验该类系统鲁棒稳定的新的充分条件,然后给出了离散区间系统鲁棒控制器存在的充分条件,并通过求解修正的代数Ｒｉｃｃａｔｉ方程,给出了该控制器的设计方法。     

BTT导弹横侧通道H∞鲁棒控制器设计研究

考虑到BTT导弹存在较大的耦合和不确定性,传统频域控制理论以及三通道独立设计方法不再适用于自动驾驶仪的设计.为此,研究了BTT导弹横侧通道H∞鲁棒控制器的设计问题,用以抑制不确定性、外来扰动以及通道间耦合给系统带来的不利影响.首先分析了H∞鲁棒控制器设计中存在的关键问题,包括不确定性的分析及加权函数的选择;然后通过建立包含未建模不确定的被控对象、引入加权函数,得到了扩展的广义受控系统,利用Matlab鲁棒控制工具箱设计了BTT导弹横侧通道的H∞鲁棒控制器;最后通过仿真验证,表明设计的H∞鲁棒控制器具有良好的控制性能和鲁棒性.     

基于Nevanlinna-Pick插值的跟踪系统H∞鲁棒控制器设计

传统的H∞鲁棒控制器通常都是基于线性矩阵不等式求解的,因此阶次较高,不利于实现,而基于 Nevanlinna-Pick插值的H∞鲁棒控制器设计方法能够有效的解决这一问题。本文提出一种改进的同伦算法用于求解控制器设计过程中出现的非线性方程,避免了经典同伦法中逆矩阵的求解。针对某一跟踪系统设计基于 Nevanlinna-Pick插值的H∞鲁棒控制器,通过阶跃响应和正弦信号的跟踪响应可以看出,与高阶超前滞后校正环节相比,前者构成闭环系统的跟踪精度要比后者的控制精度高,且具有较强的鲁棒稳定性。     

一类基于观测器的网络控制系统鲁棒控制器设计

研究一类基于状态观测器的网络控制系统鲁棒稳定性和鲁棒控制器设计问题.考虑传感器时钟驱动、控制器和执行器事件驱动以及小于等于一个采样周期的不确定时延,建立了基于状态观测器的网络控制系统增广模型.利用线性矩阵不等式方法推导出该系统鲁棒稳定的条件,进一步给出了鲁棒控制律和状态观测器设计方法.数值仿真算例证明了分析方法和结果的有效性.     

基于特征结构配置和H∞滤波器的H∞鲁棒控制器设计

提出一种基于特征结构配置和H∞滤波器的H∞鲁棒控制器设计方法，首先根据系统时域动态性能要求，采用特征结构配置方法设计出合适的状态反馈增益，然后设计H∞滤波器并进行状态估计；最后加入适当的权函数构成广义对象，并将其构造成反馈形式联接的标准的H∞鲁棒控制器设计问题，该方法不仅可实现系统时域动态响应特性，而且能保证系统的频域性能指标及鲁棒稳定性，通过对悬臂梁振动控制的数值仿真，进一步验证了该方法的有效性。     

汽车四轮转向(4WS)的二自由度鲁棒控制器设计

四累转向控制器的传统设计没有考虑汽车参数在运行过程中的变化,这样得到的控制器往往难以维持其原有设计性能指标,采用鲁棒控制理论,提出二自由度鲁棒控制器设计方法,鲁棒控制器的设计归结为一个线性矩阵不等式的求解,最后给出了一个计算实例。     

A nonparametric approach to design robust controllers for uncertain systems: Application to an air flow heating system

This paper presents an approach to design robust fixed structure controllers for uncertain systems using a finite set of measurements in the frequency domain. In traditional control system design, usually, based on measurements, a model of the plant, which is only an approximation of the physical system, is first built, and then control approaches are used to design a controller based on the identified model. Errors associated with the identification process as well as the inevitable uncertainties associated with plant parameter variations, external disturbances, measurement noise, etc. are expected to all contribute to the degradation of the performance of such a scheme. In this paper, we propose a nonparametric method that uses frequency-domain data to directly design a robust controller, for a class of uncertainties, without the need for model identification. The proposed technique, which is based on interval analysis, allows us to take into account the plant uncertainties during the controller synthesis itself. The technique relies on computing the controller parameters for which the set of all possible frequency responses of the closed-loop system are included in the envelope of a desired frequency response. Such an inclusion problem can be solved using interval techniques. The main advantages of the proposed approach are: (1) the control design does not require any mathematical model, (2) the controller is robust with respect to plant uncertainties, and (3) the controller structure can be chosen a priori, which allows us to select low-order controllers. To illustrate the proposed method and demonstrate its efficacy, an application to an air flow heating system is presented.     

一种混合神经网络飞行控制方法研究

讨论了一种基于神经网络控制的飞行控制方法。针对复杂非线性系统难以建立精确模型的特点,利用神经网络的任意非线性逼近能力进行控制器设计,首先应用神经网络在线辨识对象逆模型,进行控制系统反馈线性化;接着利用circle theorem（圆定理）设计线性PID鲁棒控制器,控制系统输出跟随系统输入,然后应用神经网路自适应逆方法设计混合控制器,最后以F-8飞机纵向飞行控制模态为研究对象进行仿真。仿真结果表明,该控制方法具有较强的自适应和抗干扰能力。     

Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators

The ever increasingly stringent performance requirements of industrial robotic applications highlight significant importance of advanced robust control designs for serial robots that are generally subject to various uncertainties and external disturbances. Therefore, this paper proposes and investigates the design and implementation of a robust adaptive fuzzy sliding mode controller in the task space for uncertain serial robotic manipulators. The sliding mode control is well known for its robustness to system parameter variations and external disturbances, and is thus a highly desirable and cost-effective approach to achieve high precision control task for serial robots. The proposed controller is designed based on a fuzzy logic approximation to accomplish trajectory tracking with high accuracy and simultaneously attenuate effects from uncertainties. In the controller, the high-frequency uncertain term is approximated by using a fuzzy logic system while the low-frequency term is adaptively updated in real time based on a parametric adaption law. The control efficacy and effectiveness of the proposed control algorithm are comparatively verified against a recently proposed conventional controller. The test results demonstrate that the proposed controller has better trajectory tracking performances and is more robust against large disturbances than the conventional controller under the same operating conditions.     

基于Backstepping 的非仿射非线性系统鲁棒控制

针对一类不确定非仿射非线性系统的跟踪控制问题, 提出一种鲁棒Backstepping 控制策略. 首先, 为利用仿 射非线性方法设计控制器, 给出一种适用于全局的非仿射非线性近似方法; 然后, 设计快速收敛非线性微分器以估计复合干扰和获取虚拟信号的微分, 进而给出不确定非仿射非线性系统的复合控制器, 其中鲁棒项和阻尼项分别用于减少逼近误差和近似方法中动态误差对系统跟踪的影响; 最后, 通过仿真实验验证了所提出方法的有效性.     

Backstepping approach for design of PID controller with guaranteed performance for micro-air UAV

Flight controllers for micro-air UAVs are generally designed using proportional-integral-derivative (PID) methods, where the tuning of gains is difficult and time-consuming, and performance is not guaranteed. In this paper, we develop a rigorous method based on the sliding mode analysis and nonlinear backstepping to design a PID controller with guaranteed performance. This technique provides the structure and gains for the PID controller, such that a robust and fast response of the UAV (unmanned aerial vehicle) for trajectory tracking is achieved. First, the second-order sliding variable errors are used in a rigorous nonlinear backstepping design to obtain guaranteed performance for the nonlinear UAV dynamics. Then, using a small angle approximation and rigorous geometric manipulations, this nonlinear design is converted into a PID controller whose structure is naturally determined through the backstepping procedure. PID gains that guarantee robust UAV performance are finally computed from the sliding mode gains and from stabilizing gains for tracking error dynamics. We prove that the desired Euler angles of the inner attitude controller loop are related to the dynamics of the outer backstepping tracker loop by inverse kinematics, which provides a seamless connection with existing built-in UAV attitude controllers. We implement the proposed method on actual UAV, and experimental flight tests prove the validity of these algorithms. It is seen that our PID design procedure yields tighter UAV performance than an existing popular PID control technique.     

An Intelligent Design for a PID Controller for Nonlinear Systems

The proportional integral derivative (PID) controller is the most frequently used controller design for industrial applications because of its favorable response and simplicity of adjustment. However, PID manual tuning is traditionally based on engineering experience, and adjusting nonlinear or unknown systems is extremely difficult. In promoting an intelligent controller design theory that can be applied to the control of various systems, this paper proposes a nonlinear control design method that involves determining the optimal solution and obtaining the transfer function of an unknown system by using sequential quadratic programming. In addition, this paper presents a case study of an induction motor V/F speed control to demonstrate the effectiveness of the proposed method based on MATLAB simulation. The results prove that the design of the proposed intelligent PID controller is more robust than traditional controller designs.     

具有未知干扰的无人机鲁棒滑模飞行控制

研究无人机飞行稳定性控制问题,由于无人机飞行控制系统存在时变外部干扰,飞行过程中升阴比变化激烈,控制稳定性难度较大。利用滑模控制良好的鲁棒能力提出一种神经网络的鲁棒飞行控制方法。因神经网络有良好非线性逼近能力,可对无人机飞行系统中的不确定进行在线逼近,并将神经网络权值误差引入到权值的自适应律中用以改善系统的动态性能。利用神经网络的组合,设计无人机鲁棒滑模飞行控制器。控制器分为两部分,一部分是等效控制器,另一部分是滑模控制器,能有效减小系统的跟踪误差。最后将所设计的鲁棒滑模控制对无人机飞行姿态控制进行仿真。仿真结果表明,新方法能提高无人机的鲁棒飞行控制能力且能实现无人机姿态的精确跟踪和稳定性控制。     

Robust performance optimization based on stochastic model errors: the stable case

Robust control aims to account for model uncertainty in design. Traditional methods for robust control typically assume knowledge of hard bounds on the system frequency response. However, this does not match well with system identification procedures which typically yield statistical confidence bounds on the estimated model. This paper explores a new procedure for obtaining a better match between robust control and system identification by using stochastic confidence bounds for robust control design. Given a nominal design, we set up an optimization problem which is aimed at reducing the statistical variability, measured in a mean square sense, from the nominal sensitivity. The proposed procedure is straightforward and leads to an easily computable solution for the final robust controller in the case of a stable plant and modest plant uncertainty. An illustrative example is provided which shows the advantages of the method. Copyright © 2002 John Wiley & Sons, Ltd.     

Robust sliding mode controllers design techniques for stabilization of multivariable time-delay systems with parameter perturbations and external disturbances

In this paper, robust delay-independent stabilization of multivariable single state-delayed systems with mismatching parameter uncertainties and matching/mismatching external disturbances are considered. To achieve this goal, two types of robust sliding mode controllers design techniques are advanced. The first is an integral sliding mode controller design modification to Shyu and Yan type controller design. The mismatching sliding conditions are parametrically obtained by using the Lyapunov-Razumikhin-Hale method and formulated in terms of some matrix norm inequalities. In the second contribution, a new combined sliding mode controller design technique for the stabilization of multivariable single state-delayed systems with mismatching parameter perturbations is advanced by using the Lyapunov-Krasovskii V-functional method. The sliding, global stability and delay-dependent β-stability conditions are parametrically obtained and formulated in terms of matrix inequalities. A sliding mode controller design example for AV-8A Harrier VTOL aircraft with lateral unstable dynamic model parameters is considered to illustrate the controller design method. Design procedures and simulation results show that our advanced method is useful, and unstable lateral dynamics is successfully stabilized by using the combined controller.     

Robust Tracking Control For A Wheeled Mobile Manipulator With Dual Arms Using Hybrid Sliding‐Mode Neural Network

In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual‐arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding‐mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding‐mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e‐modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method.